TY - JOUR

T1 - CNN-based fully automatic mitral valve extraction using CT images and existence probability maps

AU - Masuda, Yukiteru

AU - Ishikawa, Ryo

AU - Tanaka, Toru

AU - Aoyama, Gakuto

AU - Kawashima, Keitaro

AU - Chapman, James V.

AU - Asami, Masahiko

AU - Pham, Michael Huy Cuong

AU - Kofoed, Klaus Fuglsang

AU - Sakaguchi, Takuya

AU - Satoh, Kiyohide

N1 - Publisher Copyright: © 2024 Institute of Physics and Engineering in Medicine.

PY - 2024

Y1 - 2024

N2 - Objective. Accurate extraction of mitral valve shape from clinical tomographic images acquired in patients has proven useful for planning surgical and interventional mitral valve treatments. However, manual extraction of the mitral valve shape is laborious, and the existing automatic extraction methods have not been sufficiently accurate. In this paper, we propose a fully automated method of extracting mitral valve shape from computed tomography (CT) images for the all phases of the cardiac cycle. Approach. This method extracts the mitral valve shape based on DenseNet using both the original CT image and the existence probability maps of the mitral valve area inferred by U-Net as input. A total of 1585 CT images from 204 patients with various cardiac diseases including mitral regurgitation were collected and manually annotated for mitral valve region. The proposed method was trained and evaluated by 10-fold cross validation using the collected data and was compared with the method without the existence probability maps. Main results. The mean error of shape extraction error in the proposed method is 0.88 mm, which is an improvement of 0.32 mm compared with the method without the existence probability maps. Significance. We present a novel fully automatic mitral valve extraction method from input to output for all phases of 4D CT images. We suggest that the accuracy of mitral valve shape extraction is improved by using existence probability maps.

AB - Objective. Accurate extraction of mitral valve shape from clinical tomographic images acquired in patients has proven useful for planning surgical and interventional mitral valve treatments. However, manual extraction of the mitral valve shape is laborious, and the existing automatic extraction methods have not been sufficiently accurate. In this paper, we propose a fully automated method of extracting mitral valve shape from computed tomography (CT) images for the all phases of the cardiac cycle. Approach. This method extracts the mitral valve shape based on DenseNet using both the original CT image and the existence probability maps of the mitral valve area inferred by U-Net as input. A total of 1585 CT images from 204 patients with various cardiac diseases including mitral regurgitation were collected and manually annotated for mitral valve region. The proposed method was trained and evaluated by 10-fold cross validation using the collected data and was compared with the method without the existence probability maps. Main results. The mean error of shape extraction error in the proposed method is 0.88 mm, which is an improvement of 0.32 mm compared with the method without the existence probability maps. Significance. We present a novel fully automatic mitral valve extraction method from input to output for all phases of 4D CT images. We suggest that the accuracy of mitral valve shape extraction is improved by using existence probability maps.

KW - computed tomography

KW - machine learning

KW - mitral valve

KW - point cloud

KW - shape reconstruction

U2 - 10.1088/1361-6560/ad162b

DO - 10.1088/1361-6560/ad162b

M3 - Journal article

C2 - 38100829

AN - SCOPUS:85182500870

VL - 69

JO - Physics in Medicine and Biology

JF - Physics in Medicine and Biology

SN - 0031-9155

IS - 3

M1 - 035001

ER -